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Computer graphics generated by advanced rendering software come to appear so photorealistic that it has become difficult for people to visually differentiate them from photographic images. Consequently, modern computer graphics may be used as a convincing form of image forgery. Therefore, identifying computer graphics has become an important issue in image forgery detection. In this paper, a novel approach to distinguishing computer graphics from photographic images is introduced. The statistical moments of characteristic function of the image and wavelet subbands are used as the distinguishing features. In addition, we investigate the influence of different image color representations on the feature effectiveness. Specifically, the efficiency of using RGB and HSV color models is investigated. The experiments have shown that the features extracted from HSV color space, which decouples brightness from chromatic components, have demonstrated better performance than that from RGB color model.